How to Leverage Advanced Analytics for Strategy Maps
Published: 17 February 2012 ID:G00230402
Analyst(s): Christopher Iervolino
VIEW SUMMARY
CPM strategy management solutions embed advanced analytics that provide new tools to address pervasive strategy map challenges. As this functionality becomes more consumable in CPM strategy management products, you need to understand the ways they can help you.
Evidence
1 Strategy Maps: Converting Intangible Assets into Tangible Outcomes," by Robert S. Kaplan and David P. Norton.
A dashboard (or cockpit) is a reporting mechanism that aggregates and displays metrics and key performance indicators (KPIs), enabling them to be examined at a glance before further exploration via additional business intelligence (BI) tools. Dashboards are useful KPI- and metric-reporting mechanisms that enable users to quickly monitor and track performance via an esthetic user interface. They employ visualization components, such as gauges, thermometers, dials and traffic lights (see "Scorecard or Dashboard: Does It Matter" ).
A scorecard, or a balanced scorecard (BSC), is an application that helps organizations measure and align the strategic and tactical aspects of their businesses, processes and individuals via goals and targets. Scorecards require a more structured approach and framework than a dashboard, making use of methodologies, such as the BSC, European Foundation for Quality Management, value-based management or Six Sigma. The most well-known methodology is the Kaplan and Norton BSC, which suggests that an organization needs to balance the financial perspectives of performance with nonfinancial perspectives for organizational learning, customers and internal business processes (see "Scorecard or Dashboard: Does It Matter" ).
This framework links strategic goals with operational activities. Such a framework minimizes siloed, tactical approaches in which each department or function focuses on its own performance needs without looking at the bigger picture. This metrics framework should include defining the cause-and-effect relationships between leading and lagging metrics. This definition can take the form of a strategy map or some other framework that identifies the relationships among different business metrics. The metrics framework will also help create links among different analytic applications, particularly in planning. In many cases, different parts of the organization may create performance management initiatives at intermediate levels of the organizational hierarchy. Failure to connect these initiatives will result in suboptimal organizational performance, but may still deliver business benefits to those organizational groups (see "Gartner's Business Analytics Framework" ).
Advanced analytics analyzes structured and unstructured data, using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to BI, such as query and reporting, are unlikely to discover. It is frequently applied to solve business problems and identify opportunities by providing better forecasts, causal understanding, pattern identification, and process and resource optimization, as well as assist with the scenario-planning process (see "Ten Reasons to Reach Beyond Basic Business Intelligence" ).
Overview
Advanced statistical and information visualization techniques are becoming embedded in corporate performance management (CPM) strategy management products, providing new insights into performance measurement relationships. Managers need to understand how the successful use of this functionality depends on how they're used, the underlying data characteristics and the organizational capacity for data-driven decision making.
Key Findings
- Advanced analytics can help identify, explain and maintain relationships among metrics; however, its effectiveness relies on closing the analytics skills gap by cultivating end users' aptitude for interpreting data findings and enhancing organizational ability for data-driven decision making.
- Certain data characteristics can affect statistical conclusions. Business expertise is critical to identifying appropriate data for analysis, interpreting results, facilitating discussion, and maintaining overall metrics meaning and transparency.
- Understanding performance measure causality will clarify the relationship between strategic objectives and operational performance, which is a key step in establishing a more effective enterprise metrics framework.
Recommendations
- Evaluate your strategy management product to determine the availability of embedded statistical and information visualization functionality and understand product road maps or other solutions that may work in conjunction with your strategy management solution.
- Use this research to evaluate the characteristics of your data to determine the degree to which advanced analytics can provide value.
- Initiate a joint IT-business strategy-mapping pilot project to evaluate new product capabilities, identify new measurement relationships, facilitate meaningful discussions, and develop joint analytic competencies between IT and business organizations.
Analysis
Dashboards (see Note 1) measure performance. Strategy management solutions use scorecards (see Note 2) and strategy maps to go beyond dashboards and to correlate objectives with one another and with their underlying performance indicators, allowing performance to be managed. A strategy map seeks to establish cause and effect among factors that are key to financial success by linking strategy formulation to tactical execution. 1 These associations are typically discovered and maintained through casual observation, intuition and gut feel.
This method is challenging, because the relationships among these metrics are often complex, dynamic and time-intensive. If not continually analyzed and updated, a strategy map will eventually become meaningless. As a result, the relationships among metrics are typically created and maintained at either higher aggregated levels or lower operational levels. These approaches have value; however, a more comprehensive enterprise metrics framework (see Note 3) will more accurately link strategic goals and operational activities.
Some organizations have worked to address this strategy-mapping problem by applying advanced statistical and information visualization techniques to gain additional insights into the intricate cause-and-effect relationships among measures. This can be advantageous, because some of these relationships aren't intuitive enough to detect or explain without applying mathematical rigor, and are too complex to visually represent using simple hierarchical structures.
Traditionally, advanced statistical and information visualization techniques have been out of scope for most scorecarding endeavors. Advanced solutions for strategy management embed statistical and sophisticated information visualization capabilities to help end users identify, explain and maintain performance measure relationships. These capabilities promise both complex causal analysis and simultaneous scorecard transparency; these features are often mutually exclusive.
In recognition of this opportunity for improvement, CPM vendors — such as IBM (CFO Performance Dashboard v.3 Advanced Edition), a services-led IBM offering (Global Business Services) and SAS (SAS Strategy Management) — embed certain types of advanced analytics (see Note 4) into their products. However, what must organizations do to realize benefits from them? Strategy mapping requires a multifaceted effort of people, processes and technology; however, to leverage advanced analytics to improve strategy mapping, organizations must understand how it can help. Specifically, users need to know
Identifying and explaining the correlations among metrics is a challenging task. Gartner estimates that 80% of the effort behind a scorecard or dashboard initiative consists of defining the metrics and finding the right data (see "Just Give Me a CPM Dashboard" ). Most strategy management products require a predetermined knowledge of all performance measurements and an understanding of the relationships between them and their attributes (whether they're lagging or leading indicators, their weighting, etc.). Embedded functionality that helps identify these associations, represent them visually and provide a fact-based foundation for meaningful discussion could improve the accuracy and transparency of strategy maps.
Once established, maintaining the links between performance measurements is difficult. A major point of strategic scorecard failure can be their maintenance, especially during instances of sponsorship, business or environmental change. Modifications to executive management, merger and acquisition (M&A) activity, and disruptive economic, technical or competitive alterations are just a few examples of the events types that can result in changes to relationships among performance measures. Traditional strategy management products largely assume consistency over time. The introduction of analytics to monitor the association of these measures can help streamline this maintenance task.
What is the likelihood this functionality can leverage your existing data? Identifying correlations among performance measurements may be useful, but, just as often, a simple correlation analysis may result in false positives. For example, a regression analysis used to quantify the relationships among two or more metrics may indicate a strong correlation, even though there is no causal relationship. Such an example may result in nonsensical conclusions, such as on-time delivery performance having a strong positive correlation with currency fluctuations.
Analyzing the relationships among performance measurements requires statistical methods that go beyond correlation analysis to identify the causal relationships among these measurements and information visualization functionality to graphically explain them. To do this, these embedded analytic techniques also need to leverage meaning from data changes over time; so, in general, the more historical data available, the better.
For these calculations to be effective, data also needs to have an appropriate level of accuracy and granularity, so that the components that make up the measurement can be analyzed. For strategy management, the level of history, quality and granularity of the data used is largely determined by the integration capabilities of your CPM products and/or the design of your CPM applications. Additional regulatory requirements, new capabilities that allow the integration of strategic and operational planning, integrated tax provisioning needs, and requests for more-meaningful internal and external reporting have expanded the data landscape of CPM systems.
CIOs and business managers need to work together to resolve the data issues underpinning meaningful metrics. In addition to addressing data shortcomings, you need to understand that certain business data characteristics can also affect statistical interpretations. Data with these characteristics will require additional manipulation and harmonization. The common temporal characteristics of business data that may need to be addressed include the following.
Human decision making is subject to its own set of biases, especially in the face of unique fortuitous or disastrous change. In these instances, a dispassionate, data-driven approach can act as a bellwether to augment the decision-making process and identify these anomalies. As always, management needs to understand the capabilities of the statistical methods used and to properly interpret their results.
Advanced analytics can help identify, explain and maintain these relationships. More importantly, it can help provide new insights. A great deal of the value of a strategy-mapping exercise is in related discussions and the resultant insights gained. Even an unsuccessful correlation analysis can result in new management understanding.
This method is challenging, because the relationships among these metrics are often complex, dynamic and time-intensive. If not continually analyzed and updated, a strategy map will eventually become meaningless. As a result, the relationships among metrics are typically created and maintained at either higher aggregated levels or lower operational levels. These approaches have value; however, a more comprehensive enterprise metrics framework (see Note 3) will more accurately link strategic goals and operational activities.
Some organizations have worked to address this strategy-mapping problem by applying advanced statistical and information visualization techniques to gain additional insights into the intricate cause-and-effect relationships among measures. This can be advantageous, because some of these relationships aren't intuitive enough to detect or explain without applying mathematical rigor, and are too complex to visually represent using simple hierarchical structures.
Traditionally, advanced statistical and information visualization techniques have been out of scope for most scorecarding endeavors. Advanced solutions for strategy management embed statistical and sophisticated information visualization capabilities to help end users identify, explain and maintain performance measure relationships. These capabilities promise both complex causal analysis and simultaneous scorecard transparency; these features are often mutually exclusive.
In recognition of this opportunity for improvement, CPM vendors — such as IBM (CFO Performance Dashboard v.3 Advanced Edition), a services-led IBM offering (Global Business Services) and SAS (SAS Strategy Management) — embed certain types of advanced analytics (see Note 4) into their products. However, what must organizations do to realize benefits from them? Strategy mapping requires a multifaceted effort of people, processes and technology; however, to leverage advanced analytics to improve strategy mapping, organizations must understand how it can help. Specifically, users need to know
- How these methods can extend their organization's strategy management ability
- What is the likelihood this functionality can leverage your existing data
- When to apply advanced analytics to strategy mapping
- Why it's necessary to foster a culture of data-driven decision making
- Where to start
Advanced Analytics Can Extend Strategy Management Capabilities
Advanced analytics can assist with three major strategy-mapping challenges:- Identifying performance measurement relationships
- Explaining the nature of their relationships to foster consensus
- Helping to maintain them
Identifying and explaining the correlations among metrics is a challenging task. Gartner estimates that 80% of the effort behind a scorecard or dashboard initiative consists of defining the metrics and finding the right data (see "Just Give Me a CPM Dashboard" ). Most strategy management products require a predetermined knowledge of all performance measurements and an understanding of the relationships between them and their attributes (whether they're lagging or leading indicators, their weighting, etc.). Embedded functionality that helps identify these associations, represent them visually and provide a fact-based foundation for meaningful discussion could improve the accuracy and transparency of strategy maps.
Once established, maintaining the links between performance measurements is difficult. A major point of strategic scorecard failure can be their maintenance, especially during instances of sponsorship, business or environmental change. Modifications to executive management, merger and acquisition (M&A) activity, and disruptive economic, technical or competitive alterations are just a few examples of the events types that can result in changes to relationships among performance measures. Traditional strategy management products largely assume consistency over time. The introduction of analytics to monitor the association of these measures can help streamline this maintenance task.
What is the likelihood this functionality can leverage your existing data? Identifying correlations among performance measurements may be useful, but, just as often, a simple correlation analysis may result in false positives. For example, a regression analysis used to quantify the relationships among two or more metrics may indicate a strong correlation, even though there is no causal relationship. Such an example may result in nonsensical conclusions, such as on-time delivery performance having a strong positive correlation with currency fluctuations.
Analyzing the relationships among performance measurements requires statistical methods that go beyond correlation analysis to identify the causal relationships among these measurements and information visualization functionality to graphically explain them. To do this, these embedded analytic techniques also need to leverage meaning from data changes over time; so, in general, the more historical data available, the better.
For these calculations to be effective, data also needs to have an appropriate level of accuracy and granularity, so that the components that make up the measurement can be analyzed. For strategy management, the level of history, quality and granularity of the data used is largely determined by the integration capabilities of your CPM products and/or the design of your CPM applications. Additional regulatory requirements, new capabilities that allow the integration of strategic and operational planning, integrated tax provisioning needs, and requests for more-meaningful internal and external reporting have expanded the data landscape of CPM systems.
CIOs and business managers need to work together to resolve the data issues underpinning meaningful metrics. In addition to addressing data shortcomings, you need to understand that certain business data characteristics can also affect statistical interpretations. Data with these characteristics will require additional manipulation and harmonization. The common temporal characteristics of business data that may need to be addressed include the following.
Restated Data/Data Consistency
Financial results often need to be restated. For example, error corrections, regulatory requirements or M&A activity can affect performance-related statistical interpretations. In addition, data from businesses that are no longer reported on or are reported on differently over time may skew statistical analyses of performance measure linkages. Another consistency issue can result from changes in account definition, changes to accounting treatments and policies, or incorrect or inconsistent use of accounts or other metadata. These conditions are likely to require management intervention to make appropriate interpretations of statistical results, or possible data modifications and additions to aid statistical testing.Data Affected by Specific Environmental Events
The relationship among measurements can be distorted by specific events. For example, an unprecedented natural disaster or economic event may cause performance to artificially improve or decline, depending on the type of business or other circumstances (e.g., Eurozone interest rate and currency fluctuations). The effects of these events may cause periods of uncharacteristic activity.Human decision making is subject to its own set of biases, especially in the face of unique fortuitous or disastrous change. In these instances, a dispassionate, data-driven approach can act as a bellwether to augment the decision-making process and identify these anomalies. As always, management needs to understand the capabilities of the statistical methods used and to properly interpret their results.
Data Volatility
Data with extensive variation over time is generally less useful than data containing stable patterns that can be used for interpretation. This is particularly problematic when shorter time periods are measured, or when historical data is offloaded to less accessible archives. Business units operating in unstable markets, competitive situations and those experiencing other erratic factors in the business environment can cause this type of volatility. Such variations may be acute for M&A data, which typically contains only recent performance or newly acquired assets. There are also instances when advanced analytics cannot be applied. For example, traditional methods would need to be relied on for new, unique operations in which there is no historic data for statistical interpretation.When to Apply Advanced Analytics to Strategy Mapping
Although applicable to many performance measure association analysis efforts, this approach will be most effective when the strategy-mapping endeavor is complex, and when data and culture can support their use. For example, Gartner recommends the use of an enterprise metrics framework to link overall strategic goals with operational activities (see Figure 1). This provides a common set of metrics that can be consistently measured and managed across an organization and links the achievement of corporate goals and objectives with operational activities (see "Tutorial for Creating an Enterprise Metrics Framework" [Note: This document has been archived; some of its content may not reflect current conditions]).
Source: Gartner (February 2012)
Because this framework includes performance metrics across the enterprise, it may lead to a level of complexity that is difficult to implement and maintain using a traditional observational approach. This approach can benefit from advanced analytics and a data-driven decision-making culture, because the more extensive and complex the correlations are, the greater the risk that the resulting scorecard will lose transparency and management understanding, doing more harm than good. Advanced analytics can help identify, explain and maintain these relationships. More importantly, it can help provide new insights. A great deal of the value of a strategy-mapping exercise is in related discussions and the resultant insights gained. Even an unsuccessful correlation analysis can result in new management understanding.
A Culture of Data-Driven Decision Making Should Be Supported
CEOs regard data-driven decision-making capabilities as having the most potential strategic value to the business (see Figure 2 and "Executive Advisory: CEO and Senior Executive Survey" ).
Figure 2. Expected Value of Various Technology-Enabled Capabilities to Respondent Organizations, 2011-2014
Source: Gartner (February 2012)
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